Black-box Attacks to Log-based Anomaly Detection

Anomaly detection is the key to Quality of Service (QoS) in many modern systems. Logs, which record the runtime information of system, are widely used for anomaly detection. The security of the log-based anomaly detection has not been well investigated. In this paper, we conduct an empirical study on black-box attacks on log-based anomaly detection. We investigate eight different methods on log attacking and compare their performance on various log parsing methods and log anomaly detection models. We propose a method to evaluate the imperceptibility of log attacking methods. In our experiments, we evaluate the performance on the attack methods on two real log datasets. The results of our experiments show that LogBug outperforms the others in almost all situations. We also compare the imperceptibility of various attack methods and find a trade-off between performance and imperceptibility, where better attack performance means worse imperceptibility. To the best of our knowledge, this is the first work to investigate and compare the attack models on log-based anomaly detection.